Abstract
Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interaction for GNN-based fake news detectors to construct the graph, we simulate sharing behaviors to fool the detectors. Firstly, we propose a fraudster selection module to select engaged users leveraging local and global information. In addition, a post injection module guides the selected users to create shared relations by sending posts. The sharing records will be added to the social context, leading to a general attack against different detectors. Experimental results on empirical datasets demonstrate the effectiveness of GAFSI.
Abstract (translated)
基于图神经网络（GNN）的假新闻检测器应用各种方法来构建图，旨在学习分类新闻的显著特征。由于攻击者在黑盒场景中的构建细节是未知的，因此无法进行需要特定邻接矩阵的经典对抗攻击。在本文中，我们提出了第一个针对不同图结构的检测器的一般黑盒攻击框架，即通过虚假社交交互（GAFSI）进行攻击。具体来说，共享对于基于图神经网络的假新闻检测器构建图形至关重要。为了欺骗检测器，我们提出了一个欺诈者选择模块，利用本地和全局信息选择积极参与的用户。此外，一个后注入模块通过发送帖子指导选择的用户创建共享关系。共享记录将添加到社交上下文，导致对不同检测器的通用攻击。在经验数据集上的实验结果表明，GAFSI的有效性得到了充分验证。
URL
https://arxiv.org/abs/2404.15744
